• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.1B
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• pp.61-67
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• 2006

Various methods have been applied for the research to model the relationship between rainfall-runoff, which shows a strong nonlinearity. In particular, most researches to model the relationship between rainfall-runoff using artificial neural networks have used back propagation algorithm (BPA), Levenberg Marquardt (LV) and radial basis function (RBF). and They have been proved to be superior in representing the relationship between input and output showing strong nonlinearity and to be highly adaptable to rapid or significant changes in data. The theory of artificial neural networks is utilized not only for prediction but also for classifying the patterns of data and analyzing the characteristics of the patterns. Thus, the present study applied self?organizing map (SOM) based on Kohonen's network theory in order to classify the patterns of rainfall-runoff process and analyze the patterns. The results from the method proposed in the present study revealed that the method could classify the patterns of rainfall in consideration of irregular changes of temporal and spatial distribution of rainfall. In addition, according to the results from the analysis the patterns between rainfall-runoff, seven patterns of rainfall-runoff relationship with strong nonlinearity were identified by SOM.

• Journal of Soil and Groundwater Environment
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• v.13 no.1
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• pp.77-91
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• 2008

In this study, we developed a technique of applying DRASTIC, which is the most widely used tool for estimation of groundwater vulnerability to the aqueous phase contaminant infiltrated from the surface, and a groundwater flow model jointly to assess groundwater contamination potential. The developed technique is then applied to Buyeo-eup area in Buyeo-gun, Chungcheongnam-do, Korea. The input thematic data of a depth to water required in DRASTIC model is known to be the most sensitive to the output while only a few observations at a few time schedules are generally available. To overcome this practical shortcoming, both steady-state and transient groundwater level distributions are simulated using a finite difference numerical model, MODFLOW. In the application for the assessment of groundwater vulnerability, it is found that the vulnerability results from the numerical simulation of a groundwater level is much more practical compared to cokriging methods. Those advantages are, first, the results from the simulation enable a practitioner to see the temporally comprehensive vulnerabilities. The second merit of the technique is that the method considers wide variety of engaging data such as field-observed hydrogeologic parameters as well as geographic relief. The depth to water generated through geostatistical methods in the conventional method is unable to incorporate temporally variable data, that is, the seasonal variation of a recharge rate. As a result, we found that the vulnerability out of both the geostatistical method and the steady-state groundwater flow simulation are in similar patterns. By applying the transient simulation results to DRASTIC model, we also found that the vulnerability shows sharp seasonal variation due to the change of groundwater recharge. The change of the vulnerability is found to be most peculiar during summer with the highest recharge rate and winter with the lowest. Our research indicates that numerical modeling can be a useful tool for temporal as well as spatial interpolation of the depth to water when the number of the observed data is inadequate for the vulnerability assessments through the conventional techniques.

• The KIPS Transactions:PartB
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• v.9B no.2
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• pp.139-146
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• 2002

The robot soccer simulation game is a dynamic multi-agent environment. In this paper we suggest a new reinforcement learning approach to each agent's dynamic positioning in such dynamic environment. Reinforcement learning is the machine learning in which an agent learns from indirect, delayed reward an optimal policy to choose sequences of actions that produce the greatest cumulative reward. Therefore the reinforcement learning is different from supervised learning in the sense that there is no presentation of input-output pairs as training examples. Furthermore, model-free reinforcement learning algorithms like Q-learning do not require defining or learning any models of the surrounding environment. Nevertheless these algorithms can learn the optimal policy if the agent can visit every state-action pair infinitely. However, the biggest problem of monolithic reinforcement learning is that its straightforward applications do not successfully scale up to more complex environments due to the intractable large space of states. In order to address this problem, we suggest Adaptive Mediation-based Modular Q-Learning (AMMQL) as an improvement of the existing Modular Q-Learning (MQL). While simple modular Q-learning combines the results from each learning module in a fixed way, AMMQL combines them in a more flexible way by assigning different weight to each module according to its contribution to rewards. Therefore in addition to resolving the problem of large state space effectively, AMMQL can show higher adaptability to environmental changes than pure MQL. In this paper we use the AMMQL algorithn as a learning method for dynamic positioning of the robot soccer agent, and implement a robot soccer agent system called Cogitoniks.

• The Journal of Engineering Geology
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• v.15 no.1
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• pp.67-76
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• 2005

This paper presents the application of a neural network for prediction of the unconfined compressive strength from physical properties and schmidt hardness number on rock samples. To investigate the suitability of this approach, the results of analysis using a neural network are compared to predictions obtained by statistical relations. The data sets containing 55 rock sample records which are composed of sandstone and shale were assembled in Daegu area. They were used to learn the neural network model with the back-propagation teaming algorithm. The rock characteristics as the teaming input of the neural network are: schmidt hardness number, specific gravity, absorption, porosity, p-wave velocity and S-wave velocity, while the corresponding unconfined compressive strength value functions as the teaming output of the neural network. A data set containing 45 test results was used to train the networks with the back-propagation teaming algorithm. Another data set of 10 test results was used to validate the generalization and prediction capabilities of the neural network.

• (The)Korea Educational Review
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• v.23 no.4
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• pp.101-133
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• 2017

This Study aims to evaluate teacher policy in the Park Administration and to suggest tasks based on the results. To attain the aim, it describes overall teacher policy process in Park Administration and evaluates teacher policy based on the criteria which was driven from the CIPP model. The conclusions are summarized as follows. First, the environmental suitability of the situation assessment was met in general. The reason for this was that the teacher policy of the Park Geun-hye government was introduced reflecting changes in the environment and demands of policy stakeholders. The validity and feasibility of the policy objective of the input evaluation were not satisfied. There were deficiencies in the objectives of the policy measures. The objectives of the measures were not linked to the policy objectives. The budget necessary for policy enforcement were not secured. In future policy design, efforts should be made to examine whether the policy goals are valid, to secure resources necessary for policy implementation and to increase the feasibility of policies. The procedural rationality of the process evaluation and the suitability of the means were not met. Discussion with the interest group was omitted, it was not clear whether the implementation of the policy was linked to the policy goal. In the future, it will be necessary to focus on procedures such as communication and collaboration with stakeholders, to make immediate revisions in case of problems. The effectiveness and satisfaction of the output evaluation were low. Because the validity of the policy objectives, the suitability of the means, and procedural rationality were not satisfied. In the future, it will be necessary to systematically design policies and communicate with stakeholders in the execution process to prevent policy failures and actively.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.27 no.3
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• pp.127-143
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• 2022

Recently, many attempts to run numerical ocean models in cloud computing environments have been tried actively. A cloud computing environment can be an effective means to implement numerical ocean models requiring a large-scale resource or quickly preparing modeling environment for global or large-scale grids. Many commercial and private cloud computing systems provide technologies such as virtualization, high-performance CPUs and instances, ether-net based high-performance-networking, and remote direct memory access for High Performance Computing (HPC). These new features facilitate ocean modeling experimentation on commercial cloud computing systems. Many scientists and engineers expect cloud computing to become mainstream in the near future. Analysis of the performance and features of commercial cloud services for numerical modeling is essential in order to select appropriate systems as this can help to minimize execution time and the amount of resources utilized. The effect of cache memory is large in the processing structure of the ocean numerical model, which processes input/output of data in a multidimensional array structure, and the speed of the network is important due to the communication characteristics through which a large amount of data moves. In this study, the performance of the Regional Ocean Modeling System (ROMS), the High Performance Linpack (HPL) benchmarking software package, and STREAM, the memory benchmark were evaluated and compared on commercial cloud systems to provide information for the transition of other ocean models into cloud computing. Through analysis of actual performance data and configuration settings obtained from virtualization-based commercial clouds, we evaluated the efficiency of the computer resources for the various model grid sizes in the virtualization-based cloud systems. We found that cache hierarchy and capacity are crucial in the performance of ROMS using huge memory. The memory latency time is also important in the performance. Increasing the number of cores to reduce the running time for numerical modeling is more effective with large grid sizes than with small grid sizes. Our analysis results will be helpful as a reference for constructing the best computing system in the cloud to minimize time and cost for numerical ocean modeling.

• Environmental and Resource Economics Review
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• v.14 no.2
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• pp.257-290
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• 2005

Korean energy use in industrial sector has increased more rapidly than other sectors during 1980~2000 periods. Relatively higher increases in industrial sector energy consumption raise questions whether government policy of rationalization of industrial energy use has been effective. In this study, we use 80-85-90 and 90-95-00 constant price input-output table to analyze increases in industrial energy use. Using an adjusted version of structural decomposition model introduced by Chen and Rose (1990), we decompose Changes of energy use into 17 elements. We classify entire industry sector into 32 sectors including four energy sectors (coal and coal products, refined petroleum, electricity and town gas). We then analyze changes of energy use by industrial level to check differences among industrial energy demand structures. Finally, we compare three industries, electronic product manufacturing, metal manufacturing and construction, that represent technology and capital intensive, energy and material intensive and labor and capital intensive industry. As results, we find that high energy using industries make the most effort to reduce energy use. Primary metal, petrochemical and mon-metal industries show improvements in elements such as energy and material productivity, energy and material imports, energy substitution and material substitutions towards energy saving. These results imply that although those industries are heavy users of energy, they put the best effort to reduce energy use relative to other industries. We find various patterns of change in industrial energy use at industrial level. To reduce energy use, electronic product manufacturing industry needs more effort to improve technological change element while construction industry needs more effort to improve material input structure element.

• Journal of the Korean Geographical Society
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• v.42 no.1 s.118
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• pp.133-155
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• 2007

The sustainable urban development has emerged as a new paradigm of urban studies in recent years. A review of the literature of land use and transport policies in relation to sustainable development reveals a consensus that the main objectives of sustainable strategy should decrease the numbers and length of journeys, and change the land use pattern towards mixed use and high density. However, there is a lack of empirical research as to what types of policies might influence effectively the reduction in the energy consumption and emission of $CO_2$. in order to sustain urban development. This paper tries to construct the conceptual structure of the PSS(planning support system), which is designed to the simulation of the probable effects of policies and planning of different kinds in cities, and evaluate the sustainablilty level according to construct the structure of the PSS(planning support system), which is designed to the simulation of the probable effects of policies and planning of different kinds in cities, and evaluate the sustainablilty level according to the alternative scenarios. The PSS is composed of three components (input-modeling-output). The core of PSS is integrating land use-transport-environment modeling. The advantages of integrating land use-transport-environment modeling are well known, but there are very few such integrated modeling packages in practice. So this paper tries to apply TRANUS software, which is an integrated land use and transport model. The TRANUS system was calibrated to city of Yongin for the base year. The purpose of the application of TRANUS to Yongin is to examine the operability of TRANUS system in Korea. From the outputs and results of operating the system, TRANUS may be effectively used to evaluate the effects of alternative sustainable urban development policies, since sustainablilty indicators can be extracted from several aspects such as land use consumption, total trips, distance and cost, energy consumption, ratio of transport split.